Correct Prediction
In prediction markets, predictions are expressed as probabilities before an event resolves. A prediction is considered correct when the outcome it favored matches the final outcome confirmed at resolution.
Correctness is not binary in the same way as outcomes. A forecast assigning high probability to the winning outcome is considered more correct than one assigning low probability, even though both pointed in the right direction. Correct predictions can be evaluated at different points in time. Early correct predictions indicate strong foresight, while late correct predictions may reflect information that became obvious.
Aggregated across many events, correct predictions reveal patterns in market accuracy. They help identify whether markets tend to learn early, late, or inconsistently.
For analysts, correct predictions are essential for evaluating prediction markets data. They form the basis for measuring accuracy, calibration, and forecasting skill.
Why It Matters
Correct predictions demonstrate the value of prediction markets. They show how well collective belief translates into real-world outcomes.
How is a correct prediction identified in prediction markets?
A correct prediction is identified by comparing forecast probabilities with the final resolved outcome. Forecasts that assigned higher probability to the winning outcome are considered correct. The degree of correctness depends on how confident the forecast was. Resolution data provides the reference point.
Can a prediction be correct even if it was uncertain?
Yes, a prediction can be correct even if confidence was low. Assigning a modest probability to the winning outcome still reflects correct directional belief. However, low-confidence correct predictions contribute less to overall accuracy. Analysts account for this using scoring methods.
How are correct predictions used in prediction markets data analysis?
Correct predictions are used to calculate accuracy metrics and forecast error. Analysts aggregate them across events to study performance and bias. Patterns in correct predictions reveal learning behavior and market efficiency. They are central to backtesting and model evaluation.
Real-World Example
On Polymarket, if an outcome resolves as true and the market consistently priced it higher than alternatives, those forecasts count as correct predictions. Higher confidence prices contribute more to performance evaluation.
Correct Prediction and FinFeedAPI
FinFeedAPI’s Prediction Markets API provides prediction markets data needed to identify correct predictions. Analysts can align historical probability streams with final outcomes to measure correctness over time. This supports accuracy analysis, calibration studies, and forecasting evaluation. The API enables consistent assessment of correct predictions across prediction markets.